Each CUT experiment is trained up to 400 epochs, where during first 200 epochs the learning rate is kept constant and throughout the last 200 epochs it gradually decays to 0. Moreover, FastCUT model is trained up to 200 epochs with first 150 epochs keeping a constant learning rate of 0.0002 and last 50 decaying it at a constant rate. Additionally, flip-equivariance augmentation is applied when training FastCUT as described in the original paper [1]}. For calculating PatchNCE loss we extract features from 5 different layers of the \(G_{\textup {enc}}\) . Namely, RGB pixels, the first and second downsampling convolution as well as the first and the fifth residual block. These layers correspond to receptive fields of sizes \(1 \times 1\) , \(9 \times 9\) , \(15 \times 15\) , \(35 \times 35\)  and \(99 \times 99\) . For each layer's features, a 2-layer MLP is applied onto 256 randomly sampled locations in order to acquire 256-dim final features.
